Top Machine Learning Jobs in India

Design, develop and deploy AI products on Azure and Optum AI platforms using Python, Java/Spring and React. Support production deployments, and work with Oracle/MySQL databases while following company policies and procedures.

Lead design, development, testing, debugging, and maintenance of mainframe applications using COBOL, JCL, VSAM, DB2, IMS, Assembler and CICS. Implement DevOps, CI/CD, automation, unit/integration testing, performance tuning, documentation, and modern IDE workflows (VS Code + mainframe extensions). Champion source control (Endevor and Git) and leverage modern tools and AI for optimization and transformation.

Oneture Technologies is seeking technology professionals across various skill areas including Software Engineering, Frontend & Full Stack, Data & AI/ML, and Cloud & DevOps to help build digital solutions.

The Data Engineer will develop and maintain data-crawling infrastructure, work with teams to gather requirements, write efficient code, and mentor juniors.

Perform satellite image classification and time-series analysis using Sentinel and PlanetScope data; build and optimize ML classification pipelines; develop Google Earth Engine apps and geospatial tools; apply advanced geospatial and hydrological modelling; coordinate ground-truth collection; produce reports, research papers, and decision-support dashboards; support stakeholder engagement.

Manage cross-team delivery in an AI-native engineering organization, focusing on risk management, dependency resolution, and technical communication.

Lead architecture and development of the core native iOS SDK and related apps. Own feature delivery from design to public release, write high-quality tested code, collaborate with R&D and ML teams, support customers to resolve issues, and manage priorities across large-scale, distributed projects.

Lead global product marketing initiatives for InMobi's Performance DSP, focusing on go-to-market strategies, product positioning, and market differentiation.

The Senior Engineering Manager will lead engineering teams to build scalable infrastructure and products for the Databricks platform, ensuring high technical standards and supporting engineers in their development while collaborating with product leadership.

Lead and grow a regional Data & AI technical solutions team in India to ensure resiliency and performance of customer production workloads across APJ and EMEA. Oversee support operations, resolve complex data engineering and ML/streaming incidents, optimize performance, stabilize workloads on new features, engage with customers and product teams, and develop team technical expertise.

The Senior Product Manager will drive product strategy and development, collaborating with cross-functional teams to enhance customer experience, focusing on AI and SaaS solutions.

Design and architect digital modules and microarchitecture for MCU/embedded processors. Develop synthesizable RTL (Verilog/SystemVerilog), perform IP integration, FPGA prototyping, timing/power/area analysis, and collaborate across verification, DFT, firmware, and validation teams while applying AI/ML-assisted design methodologies.

The Deep Researcher will develop predictive signals for financial markets using quantitative analysis, focusing on fundamental knowledge and data exploration.

Design and deliver predictive models and automated scoring systems using machine learning and statistical methods. Work with business, pre-sales, and delivery teams to generate insights, support product creation, and manage analytics engagements. Handle large data volumes and apply advanced analytics to drive client value.

Lead post-deployment operations and optimization of Agentic AI, LLM integrations, and Python-based analytical pipelines. Supervise a data science operations team, ensure secure high-availability production systems, manage integrations with enterprise systems (SAP, Salesforce, Postgres), and drive continuous improvement, monitoring, and adoption of analytics solutions.

Design, develop, evaluate, and deploy AI/ML models and scalable solutions for business applications. Responsibilities include data preprocessing, feature engineering, model training, visualization, API development, production integration, data security, documentation, and continuous research into emerging AI/ML technologies. The role collaborates with software engineers, DevOps teams, product managers, and domain experts.

Design, build, and operate production LLM applications for personalized multilingual learning. Responsibilities include RAG and agentic pipelines, tool-calling systems, backend services, data pipelines, evaluation frameworks, monitoring, cost and latency optimization, model assessment, and open-source or sovereign LLM integration. The role also involves architecture ownership, production deployment, and potentially fine-tuning foundation models using methods such as LoRA, QLoRA, and SFT.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

Design and develop machine learning models analyzing powertrain and vehicle dynamics data for vehicle health, performance, fuel pilferage, and driver behavior use cases. Research statistical, mathematical, automotive, and physics-informed machine learning algorithms. Implement models in software, collaborate with data engineering and GUI teams for deployment and performance, and support service teams with technical guidance. The role also involves mentoring ML engineers and analysts and validating new model designs.

Lead end-to-end architecture for production-grade AI, machine learning, and Generative AI solutions on AWS. Design secure, scalable, multi-tenant systems using cloud-native, serverless, microservices, event-driven, MLOps, and RAG patterns. Guide implementations from proof of concept through production, optimize performance and costs, lead architecture reviews and client workshops, develop roadmaps and proposals, and mentor ML, Python, and cloud engineering teams across multiple engagements.

Build and deploy classical machine learning and generative AI solutions, including regression, classification, clustering, LLM, RAG, and prompt-engineering applications. Develop real-time APIs, integrate vector databases, optimize inference, monitor production models, and promote safe, explainable, unbiased AI outputs. Collaborate with business teams to translate requirements into ML use cases. Required expertise includes Python, common data science libraries, LLM frameworks, transformer architecture, NLP, REST APIs, cloud platforms, Docker, and Kubernetes.

Build, test, and document data science, ML, analytics, and GenAI solution components. Prepare and validate data, develop features, prototypes, prompts, retrieval, and evaluation datasets. Execute evaluation plans, troubleshoot defects, maintain technical artifacts, follow governance and Responsible AI practices, and contribute reusable code and documentation while collaborating with global teams.

Work on ML/DL and computer vision models for aerospace and defense (missiles, rocket telemetry, avionics). Train, test, and improve models; prepare and clean text/image/sensor datasets; build lightweight demo APIs with FastAPI/Flask; document experiments and collaborate with mentors on real-world avionics systems.

Provides technical leadership for enterprise AI and automation initiatives. Designs, builds, and scales production ML, LLM, RAG, agentic AI, MLOps, and LLMOps systems; establishes engineering, responsible AI, security, and cost standards; leads complex technical delivery; evaluates emerging technologies; mentors engineers; and partners with product and business stakeholders.

Lead enterprise AI and full-stack platform architecture across company initiatives. Design and ship microservices, REST APIs, RAG and multimodal LLM features, agentic workflows, and reusable AI components. Make end-to-end technical decisions balancing reliability, performance, and cloud costs. Mentor engineers and guide contractors and partners while establishing development, code review, and quality standards for globally distributed teams.